Ultrasound-guided thrombin injection of femoral artery pseudoaneurysms.
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether the positive initial results for ultrasound-guided percutaneous thrombin injection of pseudoaneurysms, reported predominantly in small retrospective series, would be supported in a larger prospective trial. METHODS: In April 1999, our institution adopted ultrasound-guided thrombin injection as the initial treatment for post-catheterization arterial pseudoaneurysm. Colour Doppler imaging delineates the pseudoaneurysm, its neck and the adjacent artery. A 22-gauge spinal needle is attached to a 1-mL syringe preloaded with thrombin at a concentration of 1000 U/mL. Under ultrasound guidance, the needle tip is positioned within the pseudoaneurysm, and real-time colour Doppler imaging is used to monitor the pseudoaneurysm as thrombin is slowly injected. Thrombus formation commences almost immediately, and in most cases, occlusion is complete within 5 seconds. RESULTS: We successfully treated 61 pseudoaneurysms in 61 consecutive patients. The amount of thrombin injected ranged from 20 U to 3000 U (mean 435 U); 55 pseudoaneurysms were successfully treated after a single injection, and 6 patients required a repeat injection for complete occlusion. One patient had 2 pseudoaneurysms treated on consecutive days, and 1 developed a symptomatic vasovagal reaction, which was treated conservatively. No other significant procedural complications were encountered. Fifty-nine patients had a follow-up groin Doppler sonogram between 1 and 5 days after treatment. CONCLUSION: Ultrasound-guided percutaneous thrombin injection is an effective, simple, fast and safe treatment for post-catheterization arterial pseudoaneurysm. It has replaced ultrasound-guided compression repair at our institution and is now our treatment of choice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".